Detecting and mitigating anomalies and degradation associated with devices and their operations

ABSTRACT

Physical-device anomalies and degradation can be mitigated by implementing some aspects described herein. For example, a system can determine a first data window and a second data window by applying a window function to streaming data. The system can determine a first principal eigenvector of the first data window and a first principal eigenvector of the second data window. The system can determine an angle change between the first principal eigenvectors of the two data windows. The system can then detect an anomaly based on determining that the angle change exceeds a predefined angle-change threshold. Additionally or alternatively, the system may compare the first principal eigenvector for the second data window to a baseline value to determine an absolute angle associated with the second data window. The system can then detect a degradation based on determining that the absolute angle exceeds a predefined absolute-angle threshold.

REFERENCE TO RELATED APPLICATION

This claims the benefit of priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/858,498, filed Jun. 7, 2019, andto U.S. Provisional Patent Application No. 62/900,214, filed Sep. 13,2019, the entirety of each of which is hereby incorporated by referenceherein.

TECHNICAL FIELD

The present disclosure relates generally to diagnostic analysis of aphysical device. More specifically, but not by way of limitation, thisdisclosure relates to detecting and mitigating anomalies and degradationassociated with devices and their operations.

BACKGROUND

Consumers and businesses rely on physical devices to perform a varietyof tasks. Physical devices can include one or more electronic devices,one or more mechanical devices, or both. Examples of physical devicesinclude sensors; light sources, such as light bulbs and light emittingdiodes; vehicles, such as cars, trains, busses, and airplanes; windturbines; solar panels; heaters or furnaces; pumps; valves; andcomputers, such as laptop computers, desktop computers, mobile phones,and servers. These physical devices are sometimes monitored to detectfailures and other problems therewith.

SUMMARY

One example of the present disclosure includes a system having aprocessor and a memory device comprising instructions that areexecutable by the processor for causing the processor to performoperations. The operations can include receiving streaming data from aplurality of sensors, the streaming data being multidimensional datathat includes a plurality of data points spanning a period of time. Theoperations can include determining a first data window by applying awindow function to the streaming data, the first data window spanning afirst timespan and having a predefined number of consecutive data pointsfrom the streaming data. The operations can include determining a firstprincipal eigenvector of the first data window. The operations caninclude determining a second data window by applying the window functionto the streaming data, the second data window spanning a second timespanthat is subsequent to the first timespan and having the predefinednumber of consecutive data points from the streaming data, wherein thesecond data window includes at least one data point that is differentfrom the first data window. The operations can include determining afirst principal eigenvector of the second data window. The operationscan include determining an angle change between first principaleigenvector of the first data window and the first principal eigenvectorof the second data window. The operations can include determining thatthe angle change exceeds a predefined angle-change threshold. Theoperations can include detecting an anomaly associated with a physicaldevice based on determining that the angle change exceeds the predefinedangle-change threshold, the physical device being associated with theplurality of sensors. The operations can include comparing the firstprincipal eigenvector for the second data window to a baseline unitvector to determine an absolute angle associated with the second datawindow. The operations can include determining that the absolute angleexceeds a predefined absolute-angle threshold. The operations caninclude detecting a degradation associated with the physical devicebased on determining that the absolute angle exceeds the predefinedabsolute-angle threshold. The operations can include generating one ormore electronic signals indicating at least one of the anomaly or thedegradation associated with the physical device.

Another example of the present disclosure can include a method. Themethod can include receiving streaming data from a plurality of sensors,the streaming data being multidimensional data that includes a pluralityof data points spanning a period of time. The method can includeddetermining a first data window by applying a window function to thestreaming data, the first data window spanning a first timespan andhaving a predefined number of consecutive data points from the streamingdata. The method can include determining a first principal eigenvectorof the first data window. The method can include determining a seconddata window by applying the window function to the streaming data, thesecond data window spanning a second timespan that is subsequent to thefirst timespan and having the predefined number of consecutive datapoints from the streaming data, wherein the second data window includesat least one data point that is different from the first data window.The method can include determining a first principal eigenvector of thesecond data window. The method can include determining an angle changebetween first principal eigenvector of the first data window and thefirst principal eigenvector of the second data window. The method caninclude determining that the angle change exceeds a predefinedangle-change threshold. The method can include detecting an anomalyassociated with a physical device based on determining that the anglechange exceeds the predefined angle-change threshold, the physicaldevice being associated with the plurality of sensors. The method caninclude comparing the first principal eigenvector for the second datawindow to a baseline unit vector to determine an absolute angleassociated with the second data window. The method can includedetermining that the absolute angle exceeds a predefined absolute-anglethreshold. The method can include detecting a degradation associatedwith the physical device based on determining that the absolute angleexceeds the predefined absolute-angle threshold. The method can includegenerating one or more electronic signals indicating at least one of theanomaly or the degradation associated with the physical device. Some orall of the method steps may be implemented by a processor.

Yet another example of the present disclosure can include anon-transitory computer-readable medium comprising program code that isexecutable by a processor for causing the processor to performoperations. The operations can include receiving streaming data from aplurality of sensors, the streaming data being multidimensional datathat includes a plurality of data points spanning a period of time. Theoperations can include determining a first data window by applying awindow function to the streaming data, the first data window spanning afirst timespan and having a predefined number of consecutive data pointsfrom the streaming data. The operations can include determining a firstprincipal eigenvector of the first data window. The operations caninclude determining a second data window by applying the window functionto the streaming data, the second data window spanning a second timespanthat is subsequent to the first timespan and having the predefinednumber of consecutive data points from the streaming data, wherein thesecond data window includes at least one data point that is differentfrom the first data window. The operations can include determining afirst principal eigenvector of the second data window. The operationscan include determining an angle change between first principaleigenvector of the first data window and the first principal eigenvectorof the second data window. The operations can include determining thatthe angle change exceeds a predefined angle-change threshold. Theoperations can include detecting an anomaly associated with a physicaldevice based on determining that the angle change exceeds the predefinedangle-change threshold, the physical device being associated with theplurality of sensors. The operations can include comparing the firstprincipal eigenvector for the second data window to a baseline unitvector to determine an absolute angle associated with the second datawindow. The operations can include determining that the absolute angleexceeds a predefined absolute-angle threshold. The operations caninclude detecting a degradation associated with the physical devicebased on determining that the absolute angle exceeds the predefinedabsolute-angle threshold. The operations can include generating one ormore electronic signals indicating at least one of the anomaly or thedegradation associated with the physical device.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification, any or all drawings, and each claim.

The foregoing, together with other features and examples, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 depicts a block diagram of an example of a computing systemaccording to some aspects.

FIG. 2 depicts an example of devices that can communicate with eachother over an exchange system and via a network according to someaspects.

FIG. 3 depicts a block diagram of a model of an example of acommunications protocol system according to some aspects.

FIG. 4 depicts a hierarchical diagram of an example of a communicationsgrid computing system including a variety of control and worker nodesaccording to some aspects.

FIG. 5 depicts a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects.

FIG. 6 depicts a block diagram of a portion of a communications gridcomputing system including a control node and a worker node according tosome aspects.

FIG. 7 depicts a flow chart of an example of a process for executing adata analysis or processing project according to some aspects.

FIG. 8 depicts a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects.

FIG. 9 depicts a flow chart of an example of a process includingoperations performed by an event stream processing engine according tosome aspects.

FIG. 10 depicts a block diagram of an ESP system interfacing between apublishing device and multiple event subscribing devices according tosome aspects.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects.

FIG. 12 is a node-link diagram of an example of a neural networkaccording to some aspects.

FIG. 13 depicts a flow chart of an example of a process for detectingand mitigating anomalies and degradation associated with devices andtheir operations according to some aspects.

FIG. 14 depicts an example of a small angle change between two datawindows according to some aspects.

FIG. 15 depicts an example of a large angle change between two datawindows according to some aspects.

FIG. 16 depicts an example of absolute angle changes associated withmultiple data windows according to some aspects.

FIG. 17 depicts an example of an absolute angle change associated with adata window according to some aspects.

FIG. 18 depicts an example of data streams according to some aspects.

FIG. 19 depicts an example of angle changes and absolute angle changesassociated with the data streams of FIG. 18 according to some aspects.

FIG. 20 depicts an example of data streams associated with turbinesaccording to some aspects.

FIG. 21 depicts an example of a first principal component associatedwith each of the multiple turbines according to some aspects.

FIG. 22 depicts an example of angle changes and absolute anglesassociated with the first principal components of FIG. 21 according tosome aspects.

FIG. 23 depicts an example of data streams from multiple sensorsmeasuring the energy consumption of multiple lights in an environmentaccording to some aspects.

FIG. 24 depicts an example of first principal component valuesassociated with each of the lights according to some aspects.

FIG. 25 depicts an example of angle changes associated with a firstabnormality that is specific to a particular physical device accordingto some aspects.

FIG. 26 depicts an example of angle changes associated with a secondabnormality that is not specific to a particular physical deviceaccording to some aspects.

In the appended figures, similar components or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

Anomalies and degradations associated with devices and their operationscan negatively impact the performance of those devices. An anomaly is adeviation from what is standard, normal, or expected. Degradation isdifferent from an anomaly, in that degradation is an expected decline inthe physical device's performance over a period of time. In many cases,anomalies and degradations can lead to breakdowns, failures, orotherwise suboptimal performance of the physical devices. Failure totimely detect anomalies and degradations can exacerbate such problemsand can make it more difficult, time consuming, and expensive to remedythem. In some situations, failure to timely detect anomalies anddegradations can also result in hazardous conditions for operators ofthe physical devices and surrounding workers, for example if thephysical device becomes volatile, unstable, or overheats.

Certain aspects and features of the present disclosure can overcome oneor more of the abovementioned problems by detecting and mitigatinganomalies and degradations associated with devices and their operations.For example, a detection system of the present disclosure can receivestreaming data from one or more sensors associated with a physicaldevice, such as an industrial furnace. The detection system can apply asliding window to the streaming data to determine a first data windowand a second data window, where the second data window is later than thefirst data window in time. The detection system can then determine afirst principal eigenvector of the first data window and a firstprincipal eigenvector of the second data window. Having determined thefirst principal eigenvectors of each of the data windows, the detectionsystem can determine an angle change between the first principaleigenvectors of the two data windows. If the angle change exceeds apredefined angle-change threshold, the detection system can determinethat an anomaly has occurred or is occurring with respect to thephysical device. In this way, the detection system can automaticallydetect anomalies associated with the physical device.

Additionally or alternatively, the detection system can determine anabsolute angle corresponding to the second data window. The detectionsystem can then compare the absolute angle to a predefinedabsolute-angle threshold. If the absolute angle exceeds the predefinedabsolute-angle threshold, the detection system can determine thatdegradation has occurred or is occurring with respect to the physicaldevice. In this way, the detection system can automatically detectdegradations associated with the physical device.

In some examples, the detection system may not determine any othereigenvectors of a data window besides the first principal eigenvector.For example, the processor can determine the first principal eigenvectorof the first data window without determining any other eigenvectors ofthe first data window. Likewise, the processor can determine the firstprincipal eigenvector of the second data window without determining anyother eigenvectors of the second data window. This can significantlyreduce processing times and consumption of computing resources (e.g.,processing power and memory), since the processor only determines theeigenvectors used to implement the rest of the process and avoidsdetermining other eigenvectors that may be extraneous.

In some examples, the degradation system can automatically implement oneor more countermeasures configured to mitigate a detected anomaly ordegradation associated with the physical device. For example, thedetection system can generate one or more electronic signals based onthe detected anomaly or the degradation, where the electronic signalsare configured to cause the anomaly or degradation to be mitigated. Inone such example, the electronic signals can be control signals. Thedetection system can generate and transmit the control signals to thephysical device for causing the physical device to change an operationalsetting thereof, in an effort to mitigate the anomaly or degradation. Inanother example, the detection system can transmit the control signalsto an electronic component associated with the physical device forcausing the electronic component to adjust an operational setting of thephysical device, in an effort to mitigate the anomaly or degradation. Instill another example, the electronic signals may indicate a type andseverity of the anomaly or the degradation. The detection system cantransmit the electronic signal(s) to a remote computing device forcausing the remote computing device to assist with mitigating theanomaly or degradation. For example, the remote computing device candetermine a type and severity of an anomaly based on the electronicsignals, determine a countermeasure based on the type and severity ofthe anomaly, and implement the countermeasure in an effort to mitigatethe anomaly. In this way, the detection system can automatically takecorrective action in response to detected anomalies and degradations inorder to mitigate such problems.

These illustrative examples are given to introduce the reader to thegeneral subject matter discussed here and are not intended to limit thescope of the disclosed concepts. The following sections describe variousadditional features and examples with reference to the drawings in whichlike numerals indicate like elements but, like the illustrativeexamples, should not be used to limit the present disclosure.

FIGS. 1-12 depict examples of systems and methods usable in connectionwith detecting and mitigating anomalies and degradation associated withphysical devices according to some aspects. For example, FIG. 1 is ablock diagram of an example of the hardware components of a computingsystem according to some aspects. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. The computing environment 114 may include one or more othersystems. For example, computing environment 114 may include a databasesystem 118 or a communications grid 120. The computing environment 114can include one or more processing devices (e.g., distributed over oneor more networks or otherwise in communication with one another) thatmay be collectively be referred to herein as a processor or a processingdevice.

Data transmission network 100 also includes one or more network devices102. Network devices 102 may include client devices that can communicatewith computing environment 114. For example, network devices 102 maysend data to the computing environment 114 to be processed, may sendcommunications to the computing environment 114 to control differentaspects of the computing environment or the data it is processing, amongother reasons. Network devices 102 may interact with the computingenvironment 114 through a number of ways, such as, for example, over oneor more networks 108.

In some examples, network devices 102 may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP)), to the computing environment 114 vianetworks 108. For example, the network devices 102 can transmitelectronic messages, all at once or streaming over a period of time, tothe computing environment 114 via networks 108.

The network devices 102 may include network computers, sensors,databases, or other devices that may transmit or otherwise provide datato computing environment 114. For example, network devices 102 mayinclude local area network devices, such as routers, hubs, switches, orother computer networking devices. These devices may provide a varietyof stored or generated data, such as network data or data specific tothe network devices 102 themselves. Network devices 102 may also includesensors that monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devices102 may provide data they collect over time. Network devices 102 mayalso include devices within the internet of things, such as deviceswithin a home automation network. Some of these devices may be referredto as edge devices, and may involve edge-computing circuitry. Data maybe transmitted by network devices 102 directly to computing environment114 or to network-attached data stores, such as network-attached datastores 110 for storage so that the data may be retrieved later by thecomputing environment 114 or other portions of data transmission network100. For example, the network devices 102 can transmit data to anetwork-attached data store 110 for storage. The computing environment114 may later retrieve the data from the network-attached data store 110and use the data for detecting and mitigating anomalies.

Network-attached data stores 110 can store data to be processed by thecomputing environment 114 as well as any intermediate or final datagenerated by the computing system in non-volatile memory. But in certainexamples, the configuration of the computing environment 114 allows itsoperations to be performed such that intermediate and final data resultscan be stored solely in volatile memory (e.g., RAM), without arequirement that intermediate or final data results be stored tonon-volatile types of memory (e.g., disk). This can be useful in certainsituations, such as when the computing environment 114 receives ad hocqueries from a user and when responses, which are generated byprocessing large amounts of data, need to be generated dynamically(e.g., on the fly). In this situation, the computing environment 114 maybe configured to retain the processed information within memory so thatresponses can be generated for the user at different levels of detail aswell as allow a user to interactively query against this information.

Network-attached data stores 110 may store a variety of different typesof data organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data stores may includestorage other than primary storage located within computing environment114 that is directly accessible by processors located therein.Network-attached data stores may include secondary, tertiary orauxiliary storage, such as large hard drives, servers, virtual memory,among other types. Storage devices may include portable or non-portablestorage devices, optical storage devices, and various other mediumscapable of storing, containing data. A machine-readable storage mediumor computer-readable storage medium may include a non-transitory mediumin which data can be stored and that does not include carrier waves ortransitory electronic communications. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode or machine-executable instructions that may represent a procedure,a function, a subprogram, a program, a routine, a subroutine, a module,a software package, a class, or any combination of instructions, datastructures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, among others. Furthermore,the data stores may hold a variety of different types of data. Forexample, network-attached data stores 110 may hold unstructured (e.g.,raw) data.

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time-stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data or structured hierarchically according to oneor more dimensions (e.g., parameters, attributes, or variables). Forexample, data may be stored in a hierarchical data structure, such as arelational online analytical processing (ROLAP) or multidimensionalonline analytical processing (MOLAP) database, or may be stored inanother tabular form, such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the sever farms 106 or one or more servers within the server farms106. Server farms 106 can be configured to provide information in apredetermined manner. For example, server farms 106 may access data totransmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or morewebsites, sensors, as inputs from a control database, or may have beenreceived as inputs from an external system or device. Server farms 106may assist in processing the data by turning raw data into processeddata based on one or more rules implemented by the server farms. Forexample, sensor data may be analyzed to determine changes in anenvironment over time or in real-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain examples, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork 116 can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, orsystems. In some examples, the computers, servers, or systems that makeup the cloud network 116 are different from the user's own on-premisescomputers, servers, or systems. For example, the cloud network 116 mayhost an application, and a user may, via a communication network such asthe Internet, order and use the application on demand. In some examples,the cloud network 116 may host an application for detecting andmitigating anomalies.

While each device, server, and system in FIG. 1 is shown as a singledevice, multiple devices may instead be used. For example, a set ofnetwork devices can be used to transmit various communications from asingle user, or remote server 140 may include a server stack. As anotherexample, data may be processed as part of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between a device and connection management system 150,between server farms 106 and computing environment 114, or between aserver and a device) may occur over one or more networks 108. Networks108 may include one or more of a variety of different types of networks,including a wireless network, a wired network, or a combination of awired and wireless network. Examples of suitable networks include theInternet, a personal area network, a local area network (LAN), a widearea network (WAN), or a wireless local area network (WLAN). A wirelessnetwork may include a wireless interface or combination of wirelessinterfaces. As an example, a network in the one or more networks 108 mayinclude a short-range communication channel, such as a Bluetooth or aBluetooth Low Energy channel. A wired network may include a wiredinterface. The wired or wireless networks may be implemented usingrouters, access points, bridges, gateways, or the like, to connectdevices in the network 108. The networks 108 can be incorporatedentirely within or can include an intranet, an extranet, or acombination thereof. In one example, communications between two or moresystems or devices can be achieved by a secure communications protocol,such as secure sockets layer (SSL) or transport layer security (TLS). Inaddition, data or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The computing nodes in the communicationsgrid 120 and the transmission network database system 118 may share thesame processor hardware, such as processors that are located withincomputing environment 114.

In some examples, the computing environment 114, a network device 102,or both can implement one or more processes for detecting and mitigatinganomalies. For example, the computing environment 114, a network device102, or both can implement one or more versions of the processesdiscussed with respect to any of the figures.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects. Asnoted, each communication within data transmission network 100 may occurover one or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). In someexamples, the communication can include times series data. Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. In someexamples, the network device 204 may collect data either from itssurrounding environment or from other network devices (such as networkdevices 205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, and electrical current, amongothers. The sensors may be mounted to various components used as part ofa variety of different types of systems. The network devices may detectand record data related to the environment that it monitors, andtransmit that data to computing environment 214.

The network devices 204-209 may also perform processing on data itcollects before transmitting the data to the computing environment 214,or before deciding whether to transmit data to the computing environment214. For example, network devices 204-209 may determine whether datacollected meets certain rules, for example by comparing data or valuescalculated from the data and comparing that data to one or morethresholds. The network devices 204-209 may use this data or comparisonsto determine if the data is to be transmitted to the computingenvironment 214 for further use or processing. In some examples, thenetwork devices 204-209 can pre-process the data prior to transmittingthe data to the computing environment 214. For example, the networkdevices 204-209 can reformat the data before transmitting the data tothe computing environment 214 for further processing (e.g., analyzingthe data for detecting and mitigating anomalies).

Computing environment 214 may include machines 220, 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines 220,240, computing environment 214 may have only one machine or may havemore than two machines. The machines 220, 240 that make up computingenvironment 214 may include specialized computers, servers, or othermachines that are configured to individually or collectively processlarge amounts of data. The computing environment 214 may also includestorage devices that include one or more databases of structured data,such as data organized in one or more hierarchies, or unstructured data.The databases may communicate with the processing devices withincomputing environment 214 to distribute data to them. Since networkdevices may transmit data to computing environment 214, that data may bereceived by the computing environment 214 and subsequently stored withinthose storage devices. Data used by computing environment 214 may alsobe stored in data stores 235, which may also be a part of or connectedto computing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withclient devices 230 via one or more routers 225. Computing environment214 may collect, analyze or store data from or pertaining tocommunications, client device operations, client rules, oruser-associated actions stored at one or more data stores 235. Such datamay influence communication routing to the devices within computingenvironment 214, how data is stored or processed within computingenvironment 214, among other actions.

Notably, various other devices can further be used to influencecommunication routing or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include amachine 240 that is a web server. Computing environment 214 can retrievedata of interest, such as client information (e.g., product information,client rules, etc.), technical product details, news, blog posts,e-mails, forum posts, electronic documents, social media posts (e.g.,Twitter™ posts or Facebook™ posts), time series data, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices 204-209 may receive data periodically and in real time from aweb server or other source. Devices within computing environment 214 mayalso perform pre-analysis on data it receives to determine if the datareceived should be processed as part of an ongoing project. For example,as part of a project involving detecting and mitigating anomalies fromdata, the computing environment 214 can perform a pre-analysis of thedata. The pre-analysis can include determining whether the data is in acorrect format for and, if not, reformatting the data into the correctformat.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects. More specifically, FIG. 3identifies operation of a computing environment in an Open SystemsInteraction model that corresponds to various connection components. Themodel 300 shows, for example, how a computing environment, such ascomputing environment (or computing environment 214 in FIG. 2) maycommunicate with other devices in its network, and control howcommunications between the computing environment and other devices areexecuted and under what conditions.

The model 300 can include layers 302-314. The layers 302-314 arearranged in a stack. Each layer in the stack serves the layer one levelhigher than it (except for the application layer, which is the highestlayer), and is served by the layer one level below it (except for thephysical layer 302, which is the lowest layer). The physical layer 302is the lowest layer because it receives and transmits raw bites of data,and is the farthest layer from the user in a communications system. Onthe other hand, the application layer is the highest layer because itinteracts directly with a software application.

As noted, the model 300 includes a physical layer 302. Physical layer302 represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagneticcommunications. Physical layer 302 also defines protocols that maycontrol communications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (e.g.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid-computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 can define the protocol for routing within a network.In other words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid-computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt or format data based on data types known to beaccepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availability orcommunication content or formatting using the applications.

For example, a communication link can be established between two deviceson a network. One device can transmit an analog or digitalrepresentation of an electronic message that includes a data set to theother device. The other device can receive the analog or digitalrepresentation at the physical layer 302. The other device can transmitthe data associated with the electronic message through the remaininglayers 304-314. The application layer 314 can receive data associatedwith the electronic message. The application layer 314 can identify oneor more applications, such as an application for detecting andmitigating anomalies, to which to transmit data associated with theelectronic message. The application layer 314 can transmit the data tothe identified application.

Intra-network connection components 322, 324 can operate in lowerlevels, such as physical layer 302 and link layer 304, respectively. Forexample, a hub can operate in the physical layer, a switch can operatein the physical layer, and a router can operate in the network layer.Inter-network connection components 326, 328 are shown to operate onhigher levels, such as layers 306-314. For example, routers can operatein the network layer and network devices can operate in the transport,session, presentation, and application layers.

A computing environment 330 can interact with or operate on, in variousexamples, one, more, all or any of the various layers. For example,computing environment 330 can interact with a hub (e.g., via the linklayer) to adjust which devices the hub communicates with. The physicallayer 302 may be served by the link layer 304, so it may implement suchdata from the link layer 304. For example, the computing environment 330may control which devices from which it can receive data. For example,if the computing environment 330 knows that a certain network device hasturned off, broken, or otherwise become unavailable or unreliable, thecomputing environment 330 may instruct the hub to prevent any data frombeing transmitted to the computing environment 330 from that networkdevice. Such a process may be beneficial to avoid receiving data that isinaccurate or that has been influenced by an uncontrolled environment.As another example, computing environment 330 can communicate with abridge, switch, router or gateway and influence which device within thesystem (e.g., system 200) the component selects as a destination. Insome examples, computing environment 330 can interact with variouslayers by exchanging communications with equipment operating on aparticular layer by routing or modifying existing communications. Inanother example, such as in a grid-computing environment, a node maydetermine how data within the environment should be routed (e.g., whichnode should receive certain data) based on certain parameters orinformation provided by other layers within the model.

The computing environment 330 may be a part of a communications gridenvironment, the communications of which may be implemented as shown inthe protocol of FIG. 3. For example, referring back to FIG. 2, one ormore of machines 220 and 240 may be part of a communicationsgrid-computing environment. A gridded computing environment may beemployed in a distributed system with non-interactive workloads wheredata resides in memory on the machines, or compute nodes. In such anenvironment, analytic code, instead of a database management system, cancontrol the processing performed by the nodes. Data is co-located bypre-distributing it to the grid nodes, and the analytic code on eachnode loads the local data into memory. Each node may be assigned aparticular task, such as a portion of a processing project, or toorganize or control other nodes within the grid. For example, each nodemay be assigned a portion of a processing task for detecting andmitigating anomalies.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system 400 including a variety of control and worker nodesaccording to some aspects. Communications grid computing system 400includes three control nodes and one or more worker nodes.Communications grid computing system 400 includes control nodes 402,404, and 406. The control nodes are communicatively connected viacommunication paths 451, 453, and 455. The control nodes 402-406 maytransmit information (e.g., related to the communications grid ornotifications) to and receive information from each other. Althoughcommunications grid computing system 400 is shown in FIG. 4 as includingthree control nodes, the communications grid may include more or lessthan three control nodes.

Communications grid computing system 400 (which can be referred to as a“communications grid”) also includes one or more worker nodes. Shown inFIG. 4 are six worker nodes 410-420. Although FIG. 4 shows six workernodes, a communications grid can include more or less than six workernodes. The number of worker nodes included in a communications grid maybe dependent upon how large the project or data set is being processedby the communications grid, the capacity of each worker node, the timedesignated for the communications grid to complete the project, amongothers. Each worker node within the communications grid computing system400 may be connected (wired or wirelessly, and directly or indirectly)to control nodes 402-406. Each worker node may receive information fromthe control nodes (e.g., an instruction to perform work on a project)and may transmit information to the control nodes (e.g., a result fromwork performed on a project). Furthermore, worker nodes may communicatewith each other directly or indirectly. For example, worker nodes maytransmit data between each other related to a job being performed or anindividual task within a job being performed by that worker node. Insome examples, worker nodes may not be connected (communicatively orotherwise) to certain other worker nodes. For example, a worker node 410may only be able to communicate with a particular control node 402. Theworker node 410 may be unable to communicate with other worker nodes412-420 in the communications grid, even if the other worker nodes412-420 are controlled by the same control node 402.

A control node 402-406 may connect with an external device with whichthe control node 402-406 may communicate (e.g., a communications griduser, such as a server or computer, may connect to a controller of thegrid). For example, a server or computer may connect to control nodes402-406 and may transmit a project or job to the node, such as a projector job related to detecting and mitigating anomalies. The project mayinclude the data set. The data set may be of any size and can include atime series. Once the control node 402-406 receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be receive or stored by a machine other than a control node 402-406(e.g., a Hadoop data node).

Control nodes 402-406 can maintain knowledge of the status of the nodesin the grid (e.g., grid status information), accept work requests fromclients, subdivide the work across worker nodes, and coordinate theworker nodes, among other responsibilities. Worker nodes 412-420 mayaccept work requests from a control node 402-406 and provide the controlnode with results of the work performed by the worker node. A grid maybe started from a single node (e.g., a machine, computer, server, etc.).This first node may be assigned or may start as the primary control node402 that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (e.g., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node 402 receivesa project, the primary control node may distribute portions of theproject to its worker nodes for execution. For example, a project fordetecting and mitigating anomalies can be initiated on communicationsgrid computing system 400. A primary control node can control the workto be performed for the project in order to complete the project asrequested or instructed. The primary control node may distribute work tothe worker nodes 412-420 based on various factors, such as which subsetsor portions of projects may be completed most efficiently and in thecorrect amount of time. For example, a worker node 412 may perform itsanalysis using at least a portion of data that is already local (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker node412-420 after each worker node 412-420 executes and completes its job.For example, the primary control node may receive a result from one ormore worker nodes 412-420, and the primary control node may organize(e.g., collect and assemble) the results received and compile them toproduce a complete result for the project received from the end user.

Any remaining control nodes, such as control nodes 404, 406, may beassigned as backup control nodes for the project. In an example, backupcontrol nodes may not control any portion of the project. Instead,backup control nodes may serve as a backup for the primary control nodeand take over as primary control node if the primary control node wereto fail. If a communications grid were to include only a single controlnode 402, and the control node 402 were to fail (e.g., the control nodeis shut off or breaks) then the communications grid as a whole may failand any project or job being run on the communications grid may fail andmay not complete. While the project may be run again, such a failure maycause a delay (severe delay in some cases, such as overnight delay) incompletion of the project. Therefore, a grid with multiple control nodes402-406, including a backup control node, may be beneficial.

In some examples, the primary control node may open a pair of listeningsockets to add another node or machine to the grid. A socket may be usedto accept work requests from clients, and the second socket may be usedto accept connections from other grid nodes. The primary control nodemay be provided with a list of other nodes (e.g., other machines,computers, servers, etc.) that can participate in the grid, and the rolethat each node can fill in the grid. Upon startup of the primary controlnode (e.g., the first node on the grid), the primary control node mayuse a network protocol to start the server process on every other nodein the grid. Command line parameters, for example, may inform each nodeof one or more pieces of information, such as: the role that the nodewill have in the grid, the host name of the primary control node, theport number on which the primary control node is accepting connectionsfrom peer nodes, among others. The information may also be provided in aconfiguration file, transmitted over a secure shell tunnel, recoveredfrom a configuration server, among others. While the other machines inthe grid may not initially know about the configuration of the grid,that information may also be sent to each other node by the primarycontrol node. Updates of the grid information may also be subsequentlysent to those nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it can check to see if it alreadyhas a connection to that other node. If it does not have a connection tothat node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. But, a hierarchy of nodes may also be determinedusing methods other than using the unique identifiers of the nodes. Forexample, the hierarchy may be predetermined, or may be assigned based onother predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404, 406 (and, for example, toother control or worker nodes 412-420 within the communications grid).Such communications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes 410-420 in the communications grid, unique identifiers of theworker nodes 410-420, or their relationships with the primary controlnode 402) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes 410-420 inthe communications grid. The backup control nodes 404, 406 may receiveand store the backup data received from the primary control node 402.The backup control nodes 404, 406 may transmit a request for such asnapshot (or other information) from the primary control node 402, orthe primary control node 402 may send such information periodically tothe backup control nodes 404, 406.

As noted, the backup data may allow a backup control node 404, 406 totake over as primary control node if the primary control node 402 failswithout requiring the communications grid to start the project over fromscratch. If the primary control node 402 fails, the backup control node404, 406 that will take over as primary control node may retrieve themost recent version of the snapshot received from the primary controlnode 402 and use the snapshot to continue the project from the stage ofthe project indicated by the backup data. This may prevent failure ofthe project as a whole.

A backup control node 404, 406 may use various methods to determine thatthe primary control node 402 has failed. In one example of such amethod, the primary control node 402 may transmit (e.g., periodically) acommunication to the backup control node 404, 406 that indicates thatthe primary control node 402 is working and has not failed, such as aheartbeat communication. The backup control node 404, 406 may determinethat the primary control node 402 has failed if the backup control nodehas not received a heartbeat communication for a certain predeterminedperiod of time. Alternatively, a backup control node 404, 406 may alsoreceive a communication from the primary control node 402 itself (beforeit failed) or from a worker node 410-420 that the primary control node402 has failed, for example because the primary control node 402 hasfailed to communicate with the worker node 410-420.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404,406) can take over for failed primary control node 402 and become thenew primary control node. For example, the new primary control node maybe chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative example, a backupcontrol node may be assigned to be the new primary control node byanother device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeexample, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeexample, the primary control node may transmit a communication to eachof the operable worker nodes still on the communications grid that eachof the worker nodes should purposefully fail also. After each of theworker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed. In someexamples, a communications grid computing system 400 can be used todetect and mitigate anomalies associated with physical devices.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects. The process may include,for example, receiving grid status information including a projectstatus of a portion of a project being executed by a node in thecommunications grid, as described in operation 502. For example, acontrol node (e.g., a backup control node connected to a primary controlnode and a worker node on a communications grid) may receive grid statusinformation, where the grid status information includes a project statusof the primary control node or a project status of the worker node. Theproject status of the primary control node and the project status of theworker node may include a status of one or more portions of a projectbeing executed by the primary and worker nodes in the communicationsgrid. The process may also include storing the grid status information,as described in operation 504. For example, a control node (e.g., abackup control node) may store the received grid status informationlocally within the control node. Alternatively, the grid statusinformation may be sent to another device for storage where the controlnode may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system 600 including a control node and a worker nodeaccording to some aspects. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viacommunication path 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain examples, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 610 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 610 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within nodes 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or a processing project according to some aspects. As describedwith respect to FIG. 6, the GESC at the control node may transmit datawith a client device (e.g., client device 630) to receive queries forexecuting a project and to respond to those queries after large amountsof data have been processed. The query may be transmitted to the controlnode, where the query may include a request for executing a project, asdescribed in operation 702. The query can contain instructions on thetype of data analysis to be performed in the project and whether theproject should be executed using the grid-based computing environment,as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10, may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects. ESPE 800 may includeone or more projects 802. A project may be described as a second-levelcontainer in an engine model managed by ESPE 800 where a thread poolsize for the project may be defined by a user. Each project of the oneor more projects 802 may include one or more continuous queries 804 thatcontain data flows, which are data transformations of incoming eventstreams. The one or more continuous queries 804 may include one or moresource windows 806 and one or more derived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeexample, there may be only one ESPE 800 for each instance of the ESPapplication, and ESPE 800 may have a unique engine name. Additionally,the one or more projects 802 may each have unique project names, andeach query may have a unique continuous query name and begin with auniquely named source window of the one or more source windows 806. ESPE800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects. As noted, the ESPE 800 (or an associated ESP application)defines how input event streams are transformed into meaningful outputevent streams. More specifically, the ESP application may define howinput event streams from publishers (e.g., network devices providingsensed data) are transformed into meaningful output event streamsconsumed by subscribers (e.g., a data analytics project being executedby a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. Variousoperations may be performed in parallel, for example, using a pluralityof threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 is a block diagram of an ESP system 1000 interfacing betweenpublishing device 1022 and event subscription devices 1024 a-c accordingto some aspects. ESP system 1000 may include ESP subsystem 1001,publishing device 1022, an event subscription device A 1024 a, an eventsubscription device B 1024 b, and an event subscription device C 1024 c.Input event streams are output to ESP subsystem 1001 by publishingdevice 1022. In alternative embodiments, the input event streams may becreated by a plurality of publishing devices. The plurality ofpublishing devices further may publish event streams to other ESPdevices. The one or more continuous queries instantiated by ESPE 800 mayanalyze and process the input event streams to form output event streamsoutput to event subscription device A 1024 a, event subscription deviceB 1024 b, and event subscription device C 1024 c. ESP system 1000 mayinclude a greater or a fewer number of event subscription devices ofevent subscription devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscription device A 1024 a, event subscriptiondevice B 1024 b, and event subscription device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of thepublishing device 1022.

ESP subsystem 1001 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscriptiondevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscription device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscription device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on publishing device 1022.The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscription devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some examples, big data is processed for an analyticsproject after the data is received and stored. In other examples,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the present disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations, suchas those in support of an ongoing manufacturing or drilling operation.An example of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, one ormore processors and one or more computer-readable mediums operablycoupled to the one or more processor. The processor is configured toexecute an ESP engine (ESPE). The computer-readable medium hasinstructions stored thereon that, when executed by the processor, causethe computing device to support the failover. An event block object isreceived from the ESPE that includes a unique identifier. A first statusof the computing device as active or standby is determined. When thefirst status is active, a second status of the computing device as newlyactive or not newly active is determined. Newly active is determinedwhen the computing device is switched from a standby status to an activestatus. When the second status is newly active, a last published eventblock object identifier that uniquely identifies a last published eventblock object is determined. A next event block object is selected from anon-transitory computer-readable medium accessible by the computingdevice. The next event block object has an event block object identifierthat is greater than the determined last published event block objectidentifier. The selected next event block object is published to anout-messaging network device. When the second status of the computingdevice is not newly active, the received event block object is publishedto the out-messaging network device. When the first status of thecomputing device is standby, the received event block object is storedin the non-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11.

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12. The neural network 1200 is represented asmultiple layers of interconnected neurons, such as neuron 1208, that canexchange data between one another. The layers include an input layer1202 for receiving input data, a hidden layer 1204, and an output layer1206 for providing a result. The hidden layer 1204 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1200. Although the neural network 1200 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1200 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1202 of the neural network 1200, andthe neural network 1200 can use the training data to tune one or morenumeric weights of the neural network 1200. In some examples, the neuralnetwork 1200 can be trained using backpropagation. Backpropagation caninclude determining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1200 and adesired output of the neural network 1200. Based on the gradient, one ormore numeric weights of the neural network 1200 can be updated to reducethe difference, thereby increasing the accuracy of the neural network1200. This process can be repeated multiple times to train the neuralnetwork 1200. For example, this process can be repeated hundreds orthousands of times to train the neural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1200 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1200. This can allow for information to persistwithin the recurrent neural network. For example, a recurrent neuralnetwork can determine an output based at least partially on informationthat the recurrent neural network has seen before, giving the recurrentneural network the ability to use previous input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1200. Each subsequent layer of the neural network1200 can repeat this process until the neural network 1200 outputs afinal result at the output layer 1206. For example, the neural network1200 can receive a vector of numbers as an input at the input layer1202. The neural network 1200 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1200. The neural network 1200 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the following equation:y=max(x,0)where y is the output and x is an input value from the weighted vector.The transformed output can be supplied to a subsequent layer, such asthe hidden layer 1204, of the neural network 1200. The subsequent layerof the neural network 1200 can receive the transformed output, multiplythe transformed output by a matrix of numeric weights and anonlinearity, and provide the result to yet another layer of the neuralnetwork 1200. This process continues until the neural network 1200outputs a final result at the output layer 1206.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

FIG. 13 depicts a flow chart of an example of a process for detectingand mitigating anomalies and degradation associated with physicaldevices according to some aspects. Other examples can involve moreoperations, fewer operations, different operations, or a different orderof the operations shown in FIG. 13.

In block 1302, a processor receives streaming data from a group ofsensors associated with a physical device. The streaming data can bemultidimensional data that includes multiple data points spanning aperiod of time. For example, the streaming data can be time series datahaving magnitude and time dimensions.

The group of sensors can include any number and combination of thesensor types described above. In some examples, the sensors can beconfigured for sensing one or more characteristics of the physicaldevice. This can enable the processor to detect anomalies anddegradation associated with the physical device based on the streamingdata from the sensors. As one particular example, the physical devicecan include a machine. A machine is a mechanically, electrically, orelectronically operated device for performing a task. Examples ofmachines can include vehicles, furnaces, pumps, heaters, wind turbines,computers, or any combination of these. The sensors can detect one ormore characteristics of the machine and transmit streaming dataindicating the one or more characteristics to the processor.

In other examples, the sensors can be associated with the physicaldevice in other ways. For instance, the physical device can itself be asensor among the group of sensors. This can enable the processor todetect anomalies and degradation associated with the sensor based on thestreaming data.

In block 1304, the processor determines a first data window and a seconddata window based on the streaming data. The processor can determine adata window by applying a window function to the streaming data. Thewindow function can have a window length constraining the number ofconsecutive data points in the streaming data to be included in a datawindow. For example, a window length of 9 may generate a data windowhaving 9 consecutive data points from the streaming data.

The processor can generate the first data window by applying the windowfunction to the streaming data. The first data window can span a firsttimespan and have a predefined number of consecutive data points fromthe streaming data, where the predefined number of consecutive datapoints depends on the window length associated with the window function.For example, the first data window can span 10 milliseconds (ms) andhave 9 data points.

The processor can also generate the second data window by applying thewindow function to the streaming data. The second data window can span asecond timespan that is subsequent to the first timespan, thereby makingthe second timespan more current in time than the first timespan. Thesecond data window can also have the predefined number of consecutivedata points from the streaming data. For example, the second data windowcan also span 10 milliseconds (ms) and have 9 data points. The seconddata window has at least one data point that is different from the datapoints in the first data window. That is, the second data windowincludes at least one data point that is not included in the first datawindow. The second data window may also have at least one data pointthat is included in the first data window, such that the second datawindow is partially overlapping with the first data window.

In some examples, the processor can apply a sliding window based on thewindow function to the streaming data at successive time intervals togenerate the first data window and the second data window, such that thefirst and second data windows being consecutive data windows. Theseconsecutive data windows may differ by only a single data point.

In block 1306, the processor determines a first principal eigenvector ofthe first data window and a first principal eigenvector of the seconddata window. The first principal eigenvector of the first data windowcan characterize a first subspace associated with the first data window,and the first principal eigenvector of the second data window cancharacterize a second subspace associated with the second data window.

In some examples, the processor can determine a first principaleigenvector of a data window by performing principal component analysis(PCA) on the data window. For example, the processor can determine thefirst principal eigenvector of the first data window by performing PCAon the first data window, and can determine the first principaleigenvector of the second data window by performing PCA on the seconddata window.

In some examples, the processor may not determine any other eigenvectorsof a data window besides the first principal eigenvector. For example,the processor can determine the first principal eigenvector of the firstdata window without determining any other eigenvectors of the first datawindow. Likewise, the processor can determine the first principaleigenvector of the second data window without determining any othereigenvectors of the second data window. This can significantly reduceprocessing times and consumption of computing resources (e.g.,processing power and memory), since the processor only determines theeigenvectors used to implement the rest of the process and avoidsdetermining other eigenvectors that may be extraneous.

In block 1308, the processor determines an angle change between thefirst principal eigenvector of the first data window and the firstprincipal eigenvector of the second data window. For example, theprocessor can apply the following equation to determine the anglechange:

$\theta_{i} = {\cos^{- 1}\left( \frac{v_{i - 1} \cdot v_{i}}{{v_{i - 1}}{v_{i}}} \right)}$where v_(i-1) is the first principal eigenvector of the first datawindow, v_(i) is the first principal eigenvector of the second datawindow, and θ_(i) is the angle change.

In block 1310, the processor determines if the angle change is greaterthan or equal to a predefined angle-change threshold. For example, theprocessor can compare an angle change of 3% against a predefinedangle-change threshold of 2% to determine that the angle change exceedsthe predefined angle-change threshold.

If the processor determines that the angle change meets or exceeds thepredefined angle-change threshold, then the processor can detect ananomaly associated with the physical device at block 1312. Examples ofthe anomaly can include an error or failure of the physical device. Ifthe processor determines that the angle change is below the predefinedangle-change threshold, the processor can determine that no anomaly hasoccurred at block 1314.

As a particular example, FIG. 14 shows a first data window 1402 with 7data points and a second data window 1404 with 7 data points. Three ofthe data points overlap between the first data window 1402 and thesecond data windows 1404. A black arrow in the first data window 1402represents an angle of the first data window 1402, and a black arrow inthe second data window 1404 represents an angle of the second datawindow 1402. As shown, the angle change (θ₁) is relatively small, whichcan indicate that one or both of the data windows contain normalobservations. In contrast, a large change in these angles can indicatean anomaly in one or both of the data windows. For example, FIG. 15shows a first data window 1502 with 7 data points and a second datawindow 1504 with 7 data points. Two of the data points overlap betweenthe first data window 1502 and the second data windows 1504. A blackarrow in the first data window 1502 represents an angle of the firstdata window 1502, and a black arrow in the second data window 1504represents an angle of the second data window 1402. As shown, the anglechange (θ₂) is relatively large as a result of an abnormal observation1506 (a data point value outside a normal range) in the second datawindow 1506. This large angle change can be indicative of an anomaly.Using these principles, the processor can detect anomalies associatedwith the physical device by comparing angle changes between data windows(e.g., successive data-windows) to a predefined angle-change threshold.This can be significantly faster and consume fewer computing resourcesthan alternative approaches, such as comparing each individualdata-point value in a particular data window to a threshold value.

Referring back to FIG. 13, in some examples the process can continue toblock 1316. In block 1316, the processor determines an absolute anglecorresponding to the second data window. For example, the processor cancompare the first principal eigenvector for the second data window to abaseline unit vector, to determine an absolute angle associated with thesecond data window. A baseline unit vector is a unit vector usable as abaseline from which the absolute angle can be determined. In oneparticular example, the processor can perform this comparison byapplying the following equation to determine the absolute angle:

$\delta_{i} = {\cos^{- 1}\left( \frac{e \cdot v_{i}}{v_{i}} \right)}$where v_(i) is the first principal eigenvector of the second datawindow, e is the baseline unit vector, and δ_(i) is the absolute angle.

As a particular example, FIG. 16 shows a dashed line representing abaseline unit vector 1602. Also shown are four solid lines representingfirst principal eigenvectors 1604 a-d corresponding to four datawindows. FIG. 17 shows an example of an absolute angle (δ_(i)) betweenthe first principal eigenvector 1604 d of one of the data windows andthe baseline unit vector 1602. The processor may be able to determinethis absolute angle based on the first principal eigenvector 1604 d andthe baseline unit vector 1602, for example by applying the equationdescribed above.

Referring back to FIG. 13, in block 1318 the processor determines if theabsolute angle is greater than or equal to a predefined absolute-anglethreshold. For example, the processor can compare an absolute angle of10% against a predefined absolute-angle threshold of 8% to determinethat the absolute angle exceeds the predefined absolute-angle threshold.

If the processor determines that the absolute angle meets or exceeds thepredefined absolute-angle threshold, then the processor can detect adegradation associated with the physical device at block 1320. If theprocessor determines that the absolute angle is below the predefinedabsolute-angle threshold, the processor can determine that nodegradation has occurred at block 1322.

In some examples, the process can continue to block 1324. In block 1324,the processor generates one or more electronic signals associated with adetected anomaly, detected degradation, or both. For example, theprocessor can generate and transmit one or more electronic signalsindicating at least one of the anomaly or the degradation associatedwith the physical device.

In some examples, the electronic signals can be configured to cause anoperational problem associated with a detected anomaly to be mitigated.For example, the processor can generate and transmit an electroniccommunication over a network (e.g., a local area network or theInternet) to a remote computing device, where the electroniccommunication is configured to cause the remote computing device toassist with mitigating an anomaly.

In some such examples, the remote computing device can assist withmitigating the anomaly by determining a type and severity of the anomalybased on the electronic communication, determining a countermeasurebased on the type and severity of the anomaly, and implementing thecountermeasure. The remote computing device may determine thecountermeasure based on a predefined mapping of anomaly types andseverities to countermeasures. For example, if the anomaly is a resultof outdated software, the remote computing device can apply a softwareupdate to the physical device to mitigate the anomaly. If the anomaly isa result of overheating, the remote computing device can operate a fanor other cooling system to mitigate the anomaly. Or, if the anomaly is aresult of overheating, the remote computing device can transmit one ormore control signals to the physical device for causing the physicaldevice to enter an idle state or shutdown for a time period. If theanomaly is a result of pressure buildup, the remote computing device canoperate a valve to reduce pressure and thereby mitigate the anomaly. Ifthe anomaly is a result of an improper setting, the remote computingdevice can change an operational setting of the physical device tomitigate the anomaly. Numerous other countermeasures are possibledepending on the type and severity of the anomaly. Additionally oralternatively, the remote computing device can assist with mitigatingthe anomaly in some examples by generating an alert notifying a user ofthe remote computing device about anomaly. This may enable the user toimplement a countermeasure to mitigate the anomaly.

In some examples, the electronic signals can be display signals forgenerating a graphical user interface (GUI) on a display device, such asa liquid crystal display or light-emitting diode display. The GUI caninclude an alert for notifying a user about anomaly or degradation. Thismay enable the user to implement a countermeasure to mitigate theanomaly or degradation. An example of a countermeasure for mitigatingdegradation of the physical object may include replacing a part of thephysical object or replacing the physical object as a whole.

Some more specific examples of the process of FIG. 13 being applied invarious contexts are described below with reference to FIGS. 18-26. Inparticular, FIG. 18 depicts an example of multiple data streams1802-1810 according to some aspects. The data streams 1802-1810 can bereceived from respective sensors, designated as x1-x5 in FIG. 18, whichare associated with a physical device. For example, data stream 1802 canbe received from a first sensor (x1), data stream 1804 can be receivedfrom a second sensor (x2), data stream 1806 can be received from a thirdsensor (x3), data stream 1808 can be received from a fourth sensor (x4),and data stream 1810 can be received from a fifth sensor (x5). In theexample shown in FIG. 18, there is an abnormality 1808 in the datastream 1806, between the timespan of 425 seconds and 510 seconds. Thisabnormality 1808 can be indicative of an anomaly or degradationassociated with the physical device. Some examples of the presentdisclosure can detect such issues using one or more of the processesdescribed above. For example, FIG. 19 includes a graph 1902 depictingthe data streams 1802-1806 overlaying one another to highlight theabnormality 1808. Another graph 1904 depicts angle changes between firstprincipal components of consecutive data windows. As shown, asignificant angle change occurs roughly 20 seconds after the beginningof the abnormality 1808. A system of the present disclosure can detectsuch angle changes and responsively determine that an anomaly isoccurring or has occurred on the physical device. FIG. 19 also showsanother graph 1906 depicting absolute angle changes between firstprincipal components of data windows and a baseline value. As shown, asignificant change in absolute angle occurs roughly 20 seconds after thebeginning of the abnormality 1808. A system of the present disclosurecan detect such absolute angle changes and responsively determine that adegradation of the physical device has occurred.

FIG. 20 depicts an example of data streams associated with multipleturbines (e.g., wind or water turbines) according to some aspects. Theturbines may be in the same physical environment, such as the same windfarm, in some examples. As shown, graph 2000 depicts four data streamsoverlying one another. The four data streams are received from sensorscoupled to four turbines for measuring electrical energy produced by thefour turbines. But other examples may involve other types of sensors formeasuring other characteristics of the turbines. Given the overlappingstreaming data, it can be challenging to visually inspect the graph 200do determine if a degradation or anomaly has occurred in relation to anyof the turbines. But some examples of the present disclosure canautomatically detect such issues using one or more of the processesdescribed above. For example, FIG. 21 includes a graph 2100 showingfirst principal components derived from the data streams associated withthe four turbines. Line 2102 corresponds to the first principalcomponent values associated with the fourth turbine. As shown, line 2102has a steep drop-off between 600 seconds and 700 seconds. In someexamples, the first principal component values of the four turbines canbe compared to one another to detect such drop-offs or other significantdeviations. This may be the result of an anomaly (e.g., a failure)associated with the fourth turbine.

The steep drop-off in the first principal component values shown in FIG.21 may also result in significant angle changes between consecutivevalues of the first principal component values. An example of this isshown in graph 2202 of FIG. 22, which depicts progressively increasingangle changes beginning at around 700 seconds. In some examples, asystem of the present disclosure can detect such angle changes andresponsively determine that an anomaly is occurring or has occurred onthe physical device. This may allow for anomalies to be detected for aturbine by only tracking a single parameter (e.g., the angle change)related to that turbine, rather that comparing information aboutmultiple turbines to one another, which can be slower and morecomputationally intensive.

Additionally or alternatively, the steep drop-off of the first principalcomponent values may result in significant changes to absolute anglesbetween the first principal component values and a baseline value. Anexample of this is shown in graph 2204, which depicts progressivelyincreasing absolute angles beginning at around 700 seconds. A system ofthe present disclosure can detect such absolute angle changes andresponsively determine that a degradation of the physical device hasoccurred.

FIG. 23 depicts an example of data streams 2302-2312 from multiplesensors measuring the energy consumption of multiple light sources in anenvironment (e.g., a building or parking lot) according to some aspects.The data streams 2302-2312 are received from sensors coupled to lightsources for measuring electrical energy consumed by the light sources.But other examples may involve other types of sensors for measuringother characteristics of the light sources. In the example shown in FIG.23, there is a first abnormality in the data stream 2304 (correspondingto sensor E49549) at around the 1600 second mark. The first abnormalitycan be indicative of an anomaly or degradation associated with acorresponding light source. There is also a second abnormality in thedata stream 2304 at around the 4000 second mark. The second abnormalitymay arise from a more holistic issue, such as a power surge, impactingmultiple of the data streams 2302-2312. Some examples of the presentdisclosure can differentiate between these types of abnormalities, sothat issues specific to a particular light source can be detected andmore holistic issues can be ignored. For example, FIG. 24 includes agraph 2400 depicting first principal components derived from the datastreams associated with the light sources. The first principalcomponents are overlaying one another on the graph 2400. As shown, thereare significant variations in the first principal component associatedwith the E49549 data stream at around the 1600 second mark correspondingto the first abnormality. This may produce angle changes above apredefined angle-change threshold, for example as shown in FIG. 25, thatcan be detected by some examples of the present disclosure. In contrast,there are less pronounced variations in the first principal componentassociated with the E49549 data stream at around the 4000 second markcorresponding to the second abnormality. This may yield angle changesbelow the predefined angle-change threshold, for example as shown inFIG. 26, that can be ignored by some examples of the present disclosure.In this way, issues specific to a particular physical device can bedistinguished from environmental issues affecting multiple physicaldevices, allowing for more precise monitoring of individual physicaldevices.

In the previous description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofexamples of the technology. But various examples can be practicedwithout these specific details. The figures and description are notintended to be restrictive.

The previous description provides examples that are not intended tolimit the scope, applicability, or configuration of the disclosure.Rather, the previous description of the examples provides those skilledin the art with an enabling description for implementing an example.Various changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the technology as setforth in the appended claims.

Specific details are given in the previous description to provide athorough understanding of the examples. But the examples may bepracticed without these specific details. For example, circuits,systems, networks, processes, and other components can be shown ascomponents in block diagram form to prevent obscuring the examples inunnecessary detail. In other examples, well-known circuits, processes,algorithms, structures, and techniques may be shown without unnecessarydetail in order to avoid obscuring the examples.

Also, individual examples may have been described as a process that isdepicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart can describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. And a process can have more or feweroperations than are depicted in a figure. A process can correspond to amethod, a function, a procedure, a subroutine, a subprogram, etc. When aprocess corresponds to a function, its termination can correspond to areturn of the function to the calling function or the main function.

Systems depicted in some of the figures can be provided in variousconfigurations. In some examples, the systems can be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

The invention claimed is:
 1. A system comprising: a processor; and amemory device comprising instructions that are executable by theprocessor for causing the processor to: receive streaming data from aplurality of sensors, the streaming data being multidimensional datathat includes a plurality of data points spanning a period of time;determine a first data window by applying a window function to thestreaming data, the first data window spanning a first timespan andhaving a predefined number of consecutive data points from the streamingdata; determine a first principal eigenvector of the first data window,without determining any other eigenvectors of the first data window;determine a second data window by applying the window function to thestreaming data, the second data window spanning a second timespan thatis subsequent to the first timespan and having the predefined number ofconsecutive data points from the streaming data, wherein the second datawindow includes at least one data point that is different from the firstdata window; determine a first principal eigenvector of the second datawindow, without determining any other eigenvectors of the second datawindow; determine an angle change between first principal eigenvector ofthe first data window and the first principal eigenvector of the seconddata window; determine that the angle change exceeds a predefinedangle-change threshold; detect an anomaly associated with a physicaldevice based on determining that the angle change exceeds the predefinedangle-change threshold, the physical device being associated with theplurality of sensors; compare the first principal eigenvector for thesecond data window to a baseline unit vector to determine an absoluteangle associated with the second data window; determine that theabsolute angle exceeds a predefined absolute-angle threshold; detect adegradation associated with the physical device based on determiningthat the absolute angle exceeds the predefined absolute-angle threshold;and generate one or more electronic signals indicating at least one ofthe anomaly or the degradation associated with the physical device. 2.The system of claim 1, wherein the memory device further comprisesinstructions that are executable by the processor for causing theprocessor to determine the absolute angle by: determining a product of(i) the base unit vector and (ii) the first principal eigenvector of thesecond data window; determining a norm of the first principaleigenvector of the second data window; determining a result of dividingthe product by the norm; and determining an inverse cosine of theresult.
 3. The system of claim 1, wherein the memory device furthercomprises instructions that are executable by the processor for causingthe processor to: determine the first principal eigenvector of the firstdata window by performing principal component analysis on the first datawindow; and determine the first principal eigenvector of the second datawindow by performing principal component analysis on the second datawindow.
 4. The system of claim 1, wherein the physical device is asensor among the plurality of sensors, or the physical device is amachine to which the plurality of sensors are coupled for sensingcharacteristics of the machine.
 5. The system of claim 1, wherein theanomaly is indicative of an operational problem with the physical devicethat is distinct from the degradation.
 6. The system of claim 5, whereinthe memory device further comprises instructions that are executable bythe processor for causing the processor to generate an electroniccommunication configured to cause the operational problem to bemitigated.
 7. The system of claim 6, wherein the memory device furthercomprises instructions that are executable by the processor for causingthe processor to transmit the electronic communication over a network toa remote computing device, the electronic communication being configuredto cause the remote computing device to assist with mitigating theoperational problem.
 8. The system of claim 1, wherein the memory devicefurther comprises instructions that are executable by the processor forcausing the processor to apply a sliding window based on the windowfunction to the streaming data at successive time intervals to generatethe first data window and the second data window.
 9. The system of claim1, wherein the memory device further comprises instructions that areexecutable by the processor for causing the processor to determine theangle change by: determining a first product of (i) the first principaleigenvector of the first data window and (ii) the first principaleigenvector of the second data window; determining a second product of(i) a norm of the first principal eigenvector of the first data windowand (ii) a norm of the first principal eigenvector of the second datawindow; determining a result of dividing the first product by the secondproduct; and determining an inverse cosine of the result.
 10. A methodcomprising: receiving, by a processor, streaming data from a pluralityof sensors, the streaming data being multidimensional data that includesa plurality of data points spanning a period of time; determining, bythe processor, a first data window by applying a window function to thestreaming data, the first data window spanning a first timespan andhaving a predefined number of consecutive data points from the streamingdata; determining, by the processor, a first principal eigenvector ofthe first data window, without determining any other eigenvectors of thefirst data window; determining, by the processor, a second data windowby applying the window function to the streaming data, the second datawindow spanning a second timespan that is subsequent to the firsttimespan and having the predefined number of consecutive data pointsfrom the streaming data, wherein the second data window includes atleast one data point that is different from the first data window;determining, by the processor, a first principal eigenvector of thesecond data window, without determining any other eigenvectors of thesecond data window; determining, by the processor, an angle changebetween first principal eigenvector of the first data window and thefirst principal eigenvector of the second data window; determining, bythe processor, that the angle change exceeds a predefined angle-changethreshold; detecting, by the processor, an anomaly associated with aphysical device based on determining that the angle change exceeds thepredefined angle-change threshold, the physical device being associatedwith the plurality of sensors; comparing, by the processor, the firstprincipal eigenvector for the second data window to a baseline unitvector to determine an absolute angle associated with the second datawindow; determining, by the processor, that the absolute angle exceeds apredefined absolute-angle threshold; detecting, by the processor, adegradation associated with the physical device based on determiningthat the absolute angle exceeds the predefined absolute-angle threshold;and generating, by the processor, one or more electronic signalsindicating at least one of the anomaly or the degradation associatedwith the physical device.
 11. The method of claim 10, further comprisingdetermining the absolute angle by: determining a product of (i) the baseunit vector and (ii) the first principal eigenvector of the second datawindow; determining a norm of the first principal eigenvector of thesecond data window; determining a result of dividing the product by thenorm; and determining an inverse cosine of the result.
 12. The method ofclaim 10, further comprising: determine the first principal eigenvectorof the first data window by performing principal component analysis onthe first data window; and determine the first principal eigenvector ofthe second data window by performing principal component analysis on thesecond data window.
 13. The method of claim 10, wherein the physicaldevice is a sensor among the plurality of sensors, or the physicaldevice is a machine to which the plurality of sensors are coupled forsensing characteristics of the machine.
 14. The method of claim 10,wherein the anomaly is indicative of an operational problem with thephysical device that is distinct from the degradation.
 15. The method ofclaim 14, further comprising generating an electronic communicationconfigured to cause the operational problem to be mitigated.
 16. Themethod of claim 15, further comprising transmitting the electroniccommunication over a network to a remote computing device, theelectronic communication being configured to cause the remote computingdevice to assist with mitigating the operational problem.
 17. The methodof claim 10, further comprising applying a sliding window based on thewindow function to the streaming data at successive time intervals togenerate the first data window and the second data window.
 18. Themethod of claim 10, further comprising determining the angle change by:determining a first product of (i) the first principal eigenvector ofthe first data window and (ii) the first principal eigenvector of thesecond data window; determining a second product of (i) a norm of thefirst principal eigenvector of the first data window and (ii) a norm ofthe first principal eigenvector of the second data window; determining aresult of dividing the first product by the second product; anddetermining an inverse cosine of the result.
 19. A non-transitorycomputer-readable medium comprising program code that is executable by aprocessor for causing the processor to: receive streaming data from aplurality of sensors, the streaming data being multidimensional datathat includes a plurality of data points spanning a period of time;determine a first data window by applying a window function to thestreaming data, the first data window spanning a first timespan andhaving a predefined number of consecutive data points from the streamingdata; determine a first principal eigenvector of the first data window,without determining any other eigenvectors of the first data window;determine a second data window by applying the window function to thestreaming data, the second data window spanning a second timespan thatis subsequent to the first timespan and having the predefined number ofconsecutive data points from the streaming data, wherein the second datawindow includes at least one data point that is different from the firstdata window; determine a first principal eigenvector of the second datawindow, without determining any other eigenvectors of the second datawindow; determine an angle change between first principal eigenvector ofthe first data window and the first principal eigenvector of the seconddata window; determine that the angle change exceeds a predefinedangle-change threshold; detect an anomaly associated with a physicaldevice based on determining that the angle change exceeds the predefinedangle-change threshold, the physical device being associated with theplurality of sensors; compare the first principal eigenvector for thesecond data window to a baseline unit vector to determine an absoluteangle associated with the second data window; determine that theabsolute angle exceeds a predefined absolute-angle threshold; detect adegradation associated with the physical device based on determiningthat the absolute angle exceeds the predefined absolute-angle threshold;and generate one or more electronic signals indicating at least one ofthe anomaly or the degradation associated with the physical device. 20.The non-transitory computer-readable medium of claim 19, furthercomprising program code that is executable by the processor for causingthe processor to determine the absolute angle by: determining a productof (i) the base unit vector and (ii) the first principal eigenvector ofthe second data window; determining a norm of the first principaleigenvector of the second data window; determining a result of dividingthe product by the norm; and determining an inverse cosine of theresult.
 21. The non-transitory computer-readable medium of claim 19,further comprising program code that is executable by the processor forcausing the processor to determine the first principal eigenvector ofthe first data window by performing principal component analysis on thefirst data window; and determine the first principal eigenvector of thesecond data window by performing principal component analysis on thesecond data window.
 22. The non-transitory computer-readable medium ofclaim 19, wherein the anomaly is indicative of an operational problemwith the physical device that is distinct from the degradation.
 23. Thenon-transitory computer-readable medium of claim 22, further comprisingprogram code that is executable by the processor for causing theprocessor to generate an electronic communication configured to causethe operational problem to be mitigated.
 24. The non-transitorycomputer-readable medium of claim 23, further comprising program codethat is executable by the processor for causing the processor totransmit the electronic communication over a network to a remotecomputing device, the electronic communication being configured to causethe remote computing device to assist with mitigating the operationalproblem.
 25. The non-transitory computer-readable medium of claim 19,further comprising program code that is executable by the processor forcausing the processor to determine the angle change by: determining afirst product of (i) the first principal eigenvector of the first datawindow and (ii) the first principal eigenvector of the second datawindow; determining a second product of (i) a norm of the firstprincipal eigenvector of the first data window and (ii) a norm of thefirst principal eigenvector of the second data window; determining aresult of dividing the first product by the second product; anddetermining an inverse cosine of the result.
 26. The system of claim 7,wherein the one or more electronic signals indicate a type or a severityof the anomaly, and wherein the remote computing device is configured todetermine a countermeasure to implement for mitigating the anomaly basedon the type or the severity of the anomaly.
 27. The system of claim 1,wherein the system is configured to: analyze streaming data thatincludes dozens of data points in substantially real time to identifyone or more anomalies or one or more degradations impacting aperformance of the physical device; and generate an electronic alert inresponse to identifying the one or more anomalies or the one or moredegradations.